Method and system for predicting productivity of mud pattern type shale horizontal well

Abstract

The invention provides a method and a system for predicting the productivity of a mud pattern type shale horizontal well, and the method comprises the steps: firstly, obtaining nuclear magnetic logging data, core experiment analysis data, conventional logging data and test oil well data of target mud pattern type shale; then, geological dessert parameters and engineering dessert parameters are determined according to nuclear magnetic logging data and core experiment analysis data; wherein the geological dessert parameters comprise free hydrocarbon S1 of the target mud pattern type shale and the porosity corresponding to the condition that the nuclear magnetic transverse relaxation time is greater than 8ms; the engineering dessert parameters comprise the strength brittleness of the target mud pattern type shale. Thirdly, constructing a productivity index model according to the geological dessert parameters and the engineering dessert parameters; and finally, constructing a productivity prediction model for predicting the productivity of the target mud pattern type shale horizontal well according to the productivity index model and the test oil well data. By means of the method, the accuracy of productivity prediction of the mud pattern type shale horizontal well can be effectively improved, and therefore accurate prediction of the productivity of the mud pattern type shale horizontal well is achieved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, specifically to a method and system for predicting the productivity of horizontal wells in mudstone-type shale formations. Background Technology

[0002] Production capacity is a comprehensive indicator for evaluating the dynamic characteristics of reservoirs during oil and gas development. It is typically a dynamic equilibrium achieved through the mutual constraints and influences of various factors on production potential. When predicting production capacity using well logging parameters, the testing data of all single-layer fracturing and stimulated reservoirs in the entire area are usually analyzed. Well logging characteristic values ​​such as natural gamma (GR), sonic transit time (AC), compensated density (DEN), resistivity (Rt), and oil saturation (So) are selected to establish the relationship between production capacity and well logging characteristic values. For low-porosity and ultra-low-permeability reservoirs, characterized by high flow resistance, low natural production capacity, and poor reservoir activation capacity, fracturing is necessary to achieve a certain level of production capacity. When predicting production capacity for such reservoirs, a multi-parameter fracturing production capacity prediction and evaluation model is established, considering reservoir electrical properties, physical properties, and thickness, while integrating external environmental parameters such as crude oil properties and fracturing scale.

[0003] Due to the unique characteristics and complexity of mudstone-type shale reservoirs and the varying development models, traditional oil well productivity prediction methods often fail to achieve the expected accuracy and cannot accurately reflect the true productivity situation. For example, technology CN117846582A proposes using first geological parameters and first production parameters obtained from vertical wells to determine a first potential index. Based on the first potential index relationship and the second geological parameters of horizontal wells, a second potential index is determined. The potential index and the horizontal well construction parameters are then normalized, and a productivity prediction model is established by combining the geological and construction parameters. Technology CN 117332884A proposes determining the initial production of shale horizontal wells based on formation quality factors, fracturing intensity, construction displacement, fracturing intensity factors, and horizontal section length. Technology CN115330060A proposes constructing a shale oil horizontal well productivity evaluation index based on reservoir geology, fluid properties, and fracturing engineering parameters, and establishing a horizontal well productivity calculation model. The related technology CN107451671B proposes to establish an initial production capacity model for shale formations after fracturing based on geological sweet spot parameters and engineering sweet spot parameters, and to determine the initial production capacity of shale formations after fracturing.

[0004] The aforementioned technologies utilize geological parameters and fracturing engineering parameters for production capacity prediction. However, in practical applications, it is usually necessary to conduct oil testing and discussions after drilling completion to arrive at production capacity prediction conclusions. At this time, fracturing operations have not been carried out, and fracturing parameters are lacking, which affects the accuracy of production capacity prediction and makes it impossible to accurately predict the production capacity of mudstone shale horizontal wells. Summary of the Invention

[0005] The technical problem to be solved by this invention is the inability to accurately predict the production capacity of mudstone shale horizontal wells.

[0006] To address the aforementioned technical problems, this invention provides a method and system for predicting the productivity of mud-patterned shale horizontal wells, specifically employing the following technical solution:

[0007] In a first aspect, this invention provides a method for predicting the productivity of horizontal wells in mud-textured shale, comprising: first, acquiring nuclear magnetic resonance (NMR) logging data, core experimental analysis data, conventional logging data, and well test data of the target mud-textured shale. Then, determining geological sweet spot parameters and engineering sweet spot parameters based on the NMR logging data, conventional logging data, and core experimental analysis data; wherein, the geological sweet spot parameters include: the free hydrocarbon S1 content of the target mud-textured shale and the porosity corresponding to a NMR lateral relaxation time greater than 8 ms; the engineering sweet spot parameters include: the strength brittleness of the target mud-textured shale. Next, constructing a productivity index model of the target mud-textured shale based on the geological sweet spot parameters and the engineering sweet spot parameters. Finally, constructing a productivity prediction model of the target mud-textured shale based on the productivity index model and the well test data, the productivity prediction model being used to predict the horizontal well productivity of the target mud-textured shale.

[0008] This method uses free hydrocarbon S1 (which has a good correlation with production) and porosity corresponding to a transverse relaxation time greater than 8 ms on nuclear magnetic resonance (NMR) as geological sweet spot parameters, and strength brittleness as an engineering sweet spot parameter. By combining these two parameters, a production capacity index model for mudstone-type shale is established. The production capacity index calculated by this model can effectively indicate the production capacity level. Furthermore, based on this index, a production capacity prediction model for horizontal wells in mudstone-type shale is established, thereby achieving accurate prediction of the production capacity of these wells.

[0009] Furthermore, this invention can accurately predict the production capacity of horizontal wells in mudstone-type shale formations, thereby effectively assessing resource potential, optimizing development plans, improving recovery rates, accurately conducting economic evaluations, ensuring investment returns, and providing a technical basis for increasing oilfield production. This method is a crucial link in oil and gas development, affecting its success, failure, and profitability.

[0010] In conjunction with the first aspect, in one alternative implementation, the aforementioned core experimental analysis data includes: Young's modulus and clay content; the expression for strength brittleness is:

[0011] B = ab * YMOD - c * Vcl;

[0012] Where B represents strength brittleness, YMOD represents Young's modulus, Vcl represents clay content, a represents the first parameter, b represents the second parameter, and c represents the third parameter. The first, second, and third parameters are determined by multiple regression analysis based on Young's modulus and clay content.

[0013] In conjunction with the first aspect, in one alternative implementation, the expression for the above-mentioned capacity index model is:

[0014] I = p * S1 * B;

[0015] Where I represents the productivity index, p represents the porosity corresponding to a transverse relaxation time of more than 8 ms on NMR, S1 represents free hydrocarbons, and B represents strength brittleness.

[0016] In conjunction with the first aspect, in one alternative implementation, the expression for the above capacity forecasting model is:

[0017] P ′ =d*∫(I)+e;

[0018] Among them, P ′ The value represents the predicted production capacity, I represents the production capacity index, d represents the fourth parameter, and e represents the fifth parameter. The fourth and fifth parameters are determined by regression analysis based on well test data.

[0019] In conjunction with the first aspect, in one alternative implementation, the first parameter is 3.005, the second parameter is 0.094, and the third parameter is 0.0255.

[0020] In conjunction with the first aspect, in one alternative implementation, the fourth parameter is 6.1755 and the fifth parameter is 23.792.

[0021] Secondly, this invention provides a production capacity prediction system for horizontal wells in mud-textured shale, comprising: an acquisition module, a parameter determination module, a production capacity index model construction module, and a production capacity prediction module. The acquisition module is used to acquire nuclear magnetic resonance (NMR) logging data, core experimental analysis data, conventional logging data, and well test data of the target mud-textured shale. The parameter determination module is used to determine geological sweet spot parameters and engineering sweet spot parameters based on the NMR logging data, conventional logging data, and core experimental analysis data; wherein the geological sweet spot parameters include: the free hydrocarbon S1 of the target mud-textured shale and the porosity corresponding to a NMR lateral relaxation time greater than 8 ms; the engineering sweet spot parameters include: the strength brittleness of the target mud-textured shale. The production capacity index model construction module is used to construct a production capacity index model of the target mud-textured shale based on the geological sweet spot parameters and the engineering sweet spot parameters. The production capacity prediction module is used to construct a production capacity prediction model of the target mud-textured shale based on the production capacity index model and well test data, and the production capacity prediction model is used to predict the horizontal well production capacity of the target mud-textured shale.

[0022] Thirdly, an electronic device is provided, comprising: a memory and one or more processors; the memory and the processors are coupled; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method described in any of the first aspects above.

[0023] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in any of the first aspects above.

[0024] Fifthly, a computer program product is provided that, when run on a computer, causes the computer to perform the method described in any of the first aspects above.

[0025] It is understood that the beneficial effects achieved by the mud-patterned shale horizontal well productivity prediction system described in the second aspect, the electronic equipment described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect can be referred to the beneficial effects of the first aspect and any of its alternative implementations, and will not be repeated here. Attached Figure Description

[0026] Figure 1 A schematic flowchart illustrating the method for predicting the productivity of mud-patterned shale horizontal wells provided in an embodiment of the present invention;

[0027] Figure 2 A correlation coefficient diagram between horizontal well production and porosity obtained from nuclear magnetic resonance (NMR) data provided for embodiments of the present invention;

[0028] Figure 3A graph showing the relationship between the daily production per kilometer of horizontal wells and the porosity corresponding to a transverse relaxation time of more than 8 ms provided in this embodiment of the invention;

[0029] Figure 4 A graph showing the relationship between daily production per kilometer of horizontal wells and free hydrocarbon S1 is provided for an embodiment of the present invention.

[0030] Figure 5 This is a graph showing the relationship between clay content and strength brittleness provided in an embodiment of the present invention.

[0031] Figure 6 This is a graph showing the relationship between dynamic Young's modulus and strength-brittleness provided in an embodiment of the present invention.

[0032] Figure 7 This is a graph showing the relationship between calculated strength brittleness and experimental strength brittleness provided in an embodiment of the present invention.

[0033] Figure 8 A graph showing the relationship between daily production per kilometer and production capacity index of horizontal wells provided in an embodiment of the present invention;

[0034] Figure 9 A graph showing the relationship between daily production per kilometer of horizontal wells and predicted production capacity provided in an embodiment of the present invention;

[0035] Figure 10 This is a schematic diagram of the structure of the mud-patterned shale horizontal well productivity prediction system provided in an embodiment of the present invention. Detailed Implementation

[0036] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0037] Production capacity is a comprehensive indicator for evaluating the dynamic characteristics of reservoirs during oil and gas development. It is typically a dynamic equilibrium achieved through the mutual constraints and influences of various factors on production potential. When predicting production capacity using well logging parameters, the testing data of all single-layer fracturing and stimulated reservoirs in the entire area are usually analyzed. Well logging characteristic values ​​such as natural gamma (GR), sonic transit time (AC), compensated density (DEN), resistivity (Rt), and oil saturation (So) are selected to establish the relationship between production capacity and well logging characteristic values. For low-porosity and ultra-low-permeability reservoirs, characterized by high flow resistance, low natural production capacity, and poor reservoir activation capacity, fracturing is necessary to achieve a certain level of production capacity. When predicting production capacity for such reservoirs, a multi-parameter fracturing production capacity prediction and evaluation model is established, considering reservoir electrical properties, physical properties, and thickness, while integrating external environmental parameters such as crude oil properties and fracturing scale.

[0038] Due to the unique characteristics and complexity of mudstone shale reservoirs, as well as varying development models, traditional oil well productivity prediction methods often fail to achieve the expected accuracy and fail to reflect the true productivity situation. Related technologies employ geological parameters and fracturing engineering parameters for productivity prediction. However, in practical applications, post-drilling testing and discussions are typically conducted to arrive at productivity prediction conclusions. At this stage, fracturing operations have not yet been performed, resulting in a lack of fracturing parameters, which in turn affects the accuracy of the predicted productivity, making it impossible to accurately predict the productivity of mudstone shale horizontal wells.

[0039] To address the aforementioned issues, this application provides a method and system for predicting the production capacity of horizontal wells in mud-textured shale. This method uses free hydrocarbon S1 (which has a good correlation with production) and porosity corresponding to a transverse relaxation time greater than 8 ms on nuclear magnetic resonance (NMR) as geological sweet spot parameters, and strength brittleness as an engineering sweet spot parameter. By combining the geological and engineering sweet spot parameters, a production capacity index model for mud-textured shale is established. Then, based on the production capacity index model, a production capacity prediction model for horizontal wells in mud-textured shale is established, thereby achieving accurate prediction of the production capacity of horizontal wells in mud-textured shale.

[0040] The solutions provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0041] For details, see Figure 1 The above is a flowchart illustrating the method for predicting the productivity of mud-patterned shale horizontal wells provided in this embodiment of the invention. Figure 1 As shown, the method for predicting the productivity of mud-patterned shale horizontal wells provided by the present invention includes the following steps S101-S104:

[0042] S101. Obtain nuclear magnetic resonance logging data, core experimental analysis data, conventional logging data, and oil test well data of the target mudstone shale.

[0043] Specifically, the target mudstone-type shale is the mudstone-type shale where the horizontal well with the predicted production capacity is located. In one implementation method, nuclear magnetic resonance logging, core analysis experiments, conventional logging, and well testing data acquisition can be performed on the target mudstone-type shale to obtain nuclear magnetic resonance logging data, core analysis data, conventional logging data, and well testing data.

[0044] S102. Determine the geological sweet spot parameters and engineering sweet spot parameters based on nuclear magnetic resonance logging data, conventional logging data, and core experimental analysis data.

[0045] The geological sweet spot parameters include: the free hydrocarbon S1 content and the porosity corresponding to a transverse nuclear magnetic resonance relaxation time greater than 8 ms for the target mudstone. The engineering sweet spot parameters include: the strength and brittleness of the target mudstone.

[0046] Specifically, nuclear magnetic resonance (NMR) logging determines formation porosity based on the correlation between the observed signal intensity and the hydrogen nucleus content in the pore fluid. The value at time zero is directly proportional to the total hydrogen content in the formation pores. Therefore, after calibration, the signal intensity at time zero can be calibrated as the total porosity of the rock formation.

[0047] The observed porosity can be decomposed into porosity at different NMR transverse relaxation times (T2). That is, the NMR logging data acquired in S101 can include porosity corresponding to different NMR transverse relaxation times. For example, the NMR transverse relaxation times can be 0.5ms, 1ms, 2ms, 4ms, 8ms, 16ms, 32ms, and greater than 64ms.

[0048] Nuclear magnetic resonance logging detects fluids in the formation of target mudstone shale and hydrogen nuclei in the formation. Calibration can determine the correlation between porosity and productivity.

[0049] For example, Figure 2 The correlation coefficient diagram between horizontal well production and NMR porosity provided for embodiments of the present invention is shown below. Figure 2 As shown, by analyzing the relationship between porosity corresponding to different NMR lateral relaxation times in 15 horizontal wells and the total oil production 30 days before and after the peak value in the kilometer-level horizontal section, the porosity corresponding to an NMR lateral relaxation time greater than 8 ms showed the highest correlation coefficient between porosity and production capacity, at 0.601. Therefore, it can be determined that porosity corresponding to an NMR lateral relaxation time greater than 8 ms has the best correlation with production capacity.

[0050] Furthermore, Figure 3 This is a graph showing the relationship between the daily production per kilometer of horizontal wells and the porosity corresponding to a transverse relaxation time greater than 8 ms, provided as an embodiment of the present invention. Figure 3As shown, based on the correlation between the 30-day average daily oil production of the peak value in the kilometer-level horizontal section and the porosity corresponding to a lateral relaxation time greater than 8 ms on NMR, when the porosity corresponding to a lateral relaxation time greater than 8 ms on NMR is greater than 4.5%, the 30-day average daily oil production of the peak value in the kilometer-level horizontal section is greater than 10 tons. When the porosity corresponding to a lateral relaxation time greater than 8 ms on NMR is greater than 6.5%, the 30-day average daily oil production of the peak value in the kilometer-level horizontal section is greater than 15 tons. Thus, it can be determined that the porosity corresponding to a lateral relaxation time greater than 8 ms on NMR has a good correlation with the productivity of mud-patterned shale horizontal wells.

[0051] Therefore, the porosity corresponding to a nuclear magnetic resonance transverse relaxation time greater than 8 ms can be used as a geological sweet spot parameter. That is, the geological sweet spot parameters determined in the embodiments of the present invention include: the porosity corresponding to a nuclear magnetic resonance transverse relaxation time greater than 8 ms for the target mudstone.

[0052] Free hydrocarbon S1 refers to hydrocarbons that have already formed and remain in the rock in a free state in the target mudstone-type shale. The hydrocarbon production potential of the source rock is closely related to the content of free hydrocarbon S1. The size of free hydrocarbon S1 directly affects the reservoir production and is an important indicator for directly evaluating oil production capacity and predicting production capacity.

[0053] For example, Figure 4 A graph showing the relationship between daily production per kilometer of horizontal wells and free hydrocarbon S1, provided for embodiments of the present invention. Figure 4 As shown, by analyzing the free hydrocarbon S1 and the peak 30-day average daily oil production of the kilometer-level horizontal section in 15 horizontal wells, a positive correlation between free hydrocarbon S1 and oil production can be determined. Therefore, free hydrocarbon S1 can be used to characterize the reservoir oil production of mudstone shale. That is, free hydrocarbon S1 can be used as a geological sweet spot parameter, and the geological sweet spot parameter determined in the embodiments of this invention includes: free hydrocarbon S1 of the target mudstone shale.

[0054] Furthermore, based on conventional well logging data and core experimental analysis data, the brittle characteristics of mud-textured shale can be described and characterized by the stress-strain relationship in the evaluation of its fracturability. Therefore, the brittle characteristics of mud-textured shale can be characterized by strength brittleness (the difference between peak stress and residual stress). That is, strength brittleness can be used as an engineering sweet spot parameter; specifically, the engineering sweet spot parameter determined in this embodiment of the invention includes the strength brittleness of the target mud-textured shale.

[0055] Specifically, Figure 5 This is a graph showing the relationship between clay content and strength brittleness provided in an embodiment of the present invention. Figure 6 The diagram showing the relationship between dynamic Young's modulus and strength-brittleness provided in the embodiments of the present invention is as follows: Figure 5 and 6As shown, based on core experimental data analysis and sensitive parameter analysis, Young's modulus and clay content can be determined, showing a good correlation with strength brittleness (vertical sample). Therefore, an expression for strength brittleness derived from NMR logging data can be effectively constructed using Young's modulus and clay content.

[0056] In some embodiments, the core experimental analysis data described above may include Young's modulus and clay content. The expression for strength brittleness is then:

[0057] B = ab * YMOD - c * Vcl;

[0058] Where B represents strength brittleness, and YMOD represents Young's modulus, with units of 10. 9 N / m 2 Vcl represents the clay content, expressed as a% (%). a represents the first parameter, b represents the second parameter, and c represents the third parameter. The first, second, and third parameters were determined using multiple regression analysis based on Young's modulus and clay content.

[0059] In some embodiments, the first parameter can be 3.005, the second parameter can be 0.094, and the third parameter can be 0.0255. That is, the expression for strength brittleness can be:

[0060] B=3.005-0.094*YMOD-0.025*Vcl.

[0061] Figure 7 The graph showing the relationship between calculated strength brittleness and experimental strength brittleness provided in the embodiments of the present invention is as follows: Figure 7 As shown, the calculated strength brittleness is the strength brittleness calculated according to the above expression, and the experimental strength brittleness is the strength brittleness determined experimentally on the target mudstone-type shale. Figure 7 It is evident that the calculated strength brittleness exhibits a good correlation with the experimental strength brittleness. That is, the strength brittleness of the target mudstone-type shale can be effectively and accurately determined using the aforementioned expression for strength brittleness based on Young's modulus and clay content.

[0062] S103. Construct a productivity index model for the target mudstone shale based on geological sweet spot parameters and engineering sweet spot parameters.

[0063] Furthermore, based on the geological sweet spot parameters and engineering sweet spot parameters determined in S102, a productivity index model for the target mudstone can be constructed. That is, a productivity index model can be constructed based on free hydrocarbon S1, porosity corresponding to a nuclear magnetic resonance transverse relaxation time greater than 8ms, and strength brittleness.

[0064] In some embodiments, the expression for the capacity index model is:

[0065] I = p * S1 * B;

[0066] Where I represents the productivity index, p represents the porosity corresponding to a transverse relaxation time of more than 8 ms on NMR, S1 represents free hydrocarbons, and B represents strength brittleness.

[0067] For example, Figure 8 A graph showing the relationship between daily production per kilometer and production capacity index of horizontal wells provided in an embodiment of the present invention is shown below. Figure 8 As shown, when the capacity index is greater than 0.15, the peak 30-day average daily oil production at the kilometer level is greater than 10 tons; when the capacity index is greater than 0.25, the peak 30-day average daily oil production at the kilometer level is greater than 15 tons. This demonstrates a strong correlation between the capacity index and oil production (i.e., capacity).

[0068] S104. Construct a production capacity prediction model for the target mudstone shale based on the production capacity index model and well test data. The production capacity prediction model is used to predict the horizontal well production capacity of the target mudstone shale.

[0069] Finally, based on the production index model constructed in S103 and the test well data, a production prediction model can be built, and the production capacity of horizontal wells in the target mudstone shale can be predicted using this production prediction model.

[0070] In some embodiments, the expression for the capacity forecasting model can be:

[0071] P ′ =d*∫(I)+e;

[0072] Among them, P ′ The value represents the predicted production capacity, I represents the production capacity index, d represents the fourth parameter, and e represents the fifth parameter. The fourth and fifth parameters are determined by regression analysis based on well test data.

[0073] In some embodiments, the fourth parameter is 6.1755 and the fifth parameter is 23.792. That is, the expression for the capacity forecasting model can be:

[0074] P ′ =6.1755*∫(I)+23.792.

[0075] To verify the applicability and effectiveness of the above-mentioned production capacity prediction model, calculations were performed on multiple horizontal wells of the target mudstone shale, for example. Figure 9 A graph showing the relationship between daily production per kilometer and predicted production capacity of horizontal wells provided in an embodiment of the present invention is shown below. Figure 9As shown, the horizontal axis represents horizontal well 1, horizontal well 2... horizontal well 9. The vertical axis represents the average daily output per kilometer of horizontal wells over 30 days. Line 901 represents the predicted production capacity obtained according to the above production capacity prediction model, and line 902 represents the actual production capacity. It can be seen that the predicted production capacity and the actual production capacity have good consistency, that is, the production capacity prediction model provided by this embodiment of the invention has high accuracy and effectiveness.

[0076] In summary, the method for predicting the production capacity of horizontal wells in mud-textured shale, as provided in this invention, uses free hydrocarbon S1 (which has a good correlation with production) and porosity corresponding to a transverse relaxation time greater than 8 ms on nuclear magnetic resonance (NMR) as geological sweet spot parameters, and strength brittleness as an engineering sweet spot parameter. By combining these two parameters, a production capacity index model for mud-textured shale is established. The production capacity index calculated by this model can effectively indicate the production capacity level. Furthermore, based on this index, a production capacity prediction model for horizontal wells in mud-textured shale is established, thereby achieving accurate prediction of the production capacity of these wells.

[0077] Furthermore, this invention can accurately predict the production capacity of horizontal wells in mudstone-type shale formations, thereby effectively assessing resource potential, optimizing development plans, improving recovery rates, accurately conducting economic evaluations, ensuring investment returns, and providing a technical basis for increasing oilfield production. This method is a crucial link in oil and gas development, affecting its success, failure, and profitability.

[0078] This invention also provides a production capacity prediction system for mud-patterned shale horizontal wells. Figure 10 This is a schematic diagram of the structure of the mud-patterned shale horizontal well productivity prediction system provided in an embodiment of the present invention, as shown below. Figure 10 As shown, the mud-patterned shale horizontal well production prediction system 1000 includes: an acquisition module 1100, a parameter determination module 1200, a production index model construction module 1300, and a production prediction module 1400.

[0079] The acquisition module 1100 can be used to acquire nuclear magnetic resonance logging data, core experimental analysis data, conventional logging data, and oil test well data of the target mudstone shale.

[0080] The parameter determination module 1200 can be used to determine geological sweet spot parameters and engineering sweet spot parameters based on nuclear magnetic resonance logging data, conventional logging data, and core experimental analysis data. Among them, the geological sweet spot parameters include: the free hydrocarbon S1 of the target mudstone and the porosity corresponding to a nuclear magnetic resonance lateral relaxation time greater than 8ms; the engineering sweet spot parameters include: the strength and brittleness of the target mudstone.

[0081] The productivity index model construction module 1300 can be used to construct a productivity index model for a target mudstone shale based on geological sweet spot parameters and engineering sweet spot parameters.

[0082] The production capacity prediction module 1400 can be used to construct a production capacity prediction model for the target mudstone shale based on the production capacity index model and well test data. The production capacity prediction model is used to predict the horizontal well production capacity of the target mudstone shale.

[0083] This invention also provides an electronic device, which may include a display screen, a memory, and one or more processors. The display screen, memory, and processors are coupled. The memory stores computer program code, which includes computer instructions. When the processor executes the computer instructions, the electronic device can perform the mud-textured shale horizontal well productivity prediction method described in the above-described method embodiments. Of course, this electronic device includes, but is not limited to, the aforementioned display screen, memory, and one or more processors.

[0084] This invention also provides a computer-readable storage medium for storing computer instructions for running the mud-patterned shale horizontal well productivity prediction method shown in S101-S104 above.

[0085] This invention also provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the above-described method for predicting the productivity of mud-patterned shale horizontal wells.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0087] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Similar parts between the embodiments provided in this application can be referred to mutually. The specific implementation methods provided above are only a few examples under the overall concept of this application and do not constitute a limitation on the scope of protection of this application. For those skilled in the art, any other implementation methods extended from the solution of this application without creative effort shall fall within the scope of protection of this application.

Claims

1. A method for predicting the productivity of horizontal wells in mud-patterned shale formations, characterized in that, include: Acquire nuclear magnetic resonance logging data, core experimental analysis data, conventional logging data, and well test data of the target mudstone shale; Geological sweet spot parameters and engineering sweet spot parameters are determined based on the nuclear magnetic resonance logging data, the conventional logging data, and the core experimental analysis data; wherein, the geological sweet spot parameters include: the free hydrocarbon S1 of the target mudstone and the porosity corresponding to a nuclear magnetic resonance lateral relaxation time greater than 8ms; the engineering sweet spot parameters include: the strength brittleness of the target mudstone. A productivity index model for the target mudstone shale is constructed based on the geological sweet spot parameters and the engineering sweet spot parameters. Based on the production capacity index model and the test well data, a production capacity prediction model for the target mudstone shale is constructed. The production capacity prediction model is used to predict the horizontal well production capacity of the target mudstone shale.

2. The method according to claim 1, characterized in that, The core experimental analysis data includes: Young's modulus and clay content; the expression for the strength-brittleness is: B = ab * YMOD - c * Vcl; Wherein, B represents the strength brittleness, YMOD represents the Young's modulus, Vcl represents the clay content, a represents the first parameter, b represents the second parameter, and c represents the third parameter. The first parameter, the second parameter, and the third parameter are determined by multiple regression analysis based on the Young's modulus and the clay content.

3. The method according to claim 1 or 2, characterized in that, The expression for the capacity index model is: I = p * S1 * B; Where I represents the production capacity index, p represents the porosity corresponding to the NMR transverse relaxation time being greater than 8ms, S1 represents the free hydrocarbon, and B represents the strength brittleness.

4. The method according to claim 1 or 2, characterized in that, The expression for the capacity prediction model is: P ′ =d*∫(I)+e; Among them, P ′ The predicted production capacity value is represented by I, the production capacity index, d, and e. The fourth and fifth parameters are determined by regression analysis based on the test well data.

5. The method according to claim 2, characterized in that, The first parameter is 3.005, and the second parameter is: The third parameter is 0.0255.

6. The method according to claim 4, characterized in that, The fourth parameter is: 6.1755, the fifth parameter is: 23.

792.

7. A productivity prediction system for mud-patterned shale horizontal wells, characterized in that, include: The module includes an acquisition module, a parameter determination module, a capacity index model construction module, and a capacity forecasting module; among them, The acquisition module is used to acquire nuclear magnetic resonance logging data, core experimental analysis data, conventional logging data, and oil test well data of the target mudstone shale; The parameter determination module is used to determine geological sweet spot parameters and engineering sweet spot parameters based on the nuclear magnetic resonance logging data, the conventional logging data, and the core experimental analysis data; wherein, the geological sweet spot parameters include: the free hydrocarbon S1 of the target mudstone and the porosity corresponding to a nuclear magnetic resonance lateral relaxation time greater than 8ms; the engineering sweet spot parameters include: the strength brittleness of the target mudstone. The productivity index model construction module is used to construct the productivity index model of the target mudstone based on the geological sweet spot parameters and the engineering sweet spot parameters. The production capacity prediction module is used to construct a production capacity prediction model for the target mudstone shale based on the production capacity index model and the test well data. The production capacity prediction model is used to predict the horizontal well production capacity of the target mudstone shale.

8. An electronic device, characterized in that, include: A memory, one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-6.

Images